Automatic Speech Recognition
Transformers
Safetensors
English
whisper
grpo
reinforcement-learning
asr
Eval Results (legacy)
Instructions to use wrice/whisper-tiny-grpo-0029b82-cv17-tiny-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wrice/whisper-tiny-grpo-0029b82-cv17-tiny-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="wrice/whisper-tiny-grpo-0029b82-cv17-tiny-sft")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("wrice/whisper-tiny-grpo-0029b82-cv17-tiny-sft") model = AutoModelForSpeechSeq2Seq.from_pretrained("wrice/whisper-tiny-grpo-0029b82-cv17-tiny-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- a2b54de1e7293fc94b74196a58764d8f92d6d7b3554a70ad6fa2918b65c42023
- Size of remote file:
- 219 kB
- SHA256:
- 6a96ac45a112933af4c37787da53320bef73ffa55f15464324b6d287ae392ac5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.